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Characterisation of Si-crystalline PV modules by artificial neural networks

Author

Listed:
  • Almonacid, F.
  • Rus, C.
  • Hontoria, L.
  • Fuentes, M.
  • Nofuentes, G.

Abstract

In the photovoltaic field, manufacturers provide ratings for PV modules for conditions referred to as standard test conditions (STC). However, these conditions rarely occur outdoors, so the usefulness and applicability of the indoors' characterisation in standard test conditions of PV modules are a controversial issue. Therefore, to carry out photovoltaic engineering well, a suitable characterisation of PV module electrical behaviour (V–I curves) is necessary. The IDEA Research Group from Jaén University has developed a method based on artificial neural networks (ANNs) to electrical characterisation of PV modules. An ANN has been developed which is able to generate V–I curves of Si-crystalline PV modules for any irradiance and module cell temperature. The results show that the proposed ANN introduces a good accurate prediction for Si-crystalline PV modules' performance when compared with the measured values.

Suggested Citation

  • Almonacid, F. & Rus, C. & Hontoria, L. & Fuentes, M. & Nofuentes, G., 2009. "Characterisation of Si-crystalline PV modules by artificial neural networks," Renewable Energy, Elsevier, vol. 34(4), pages 941-949.
    • Handle: RePEc:eee:renene:v:34:y:2009:i:4:p:941-949
    DOI: 10.1016/j.renene.2008.06.010
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    References listed on IDEAS

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    1. Bahgat, A.B.G & Helwa, N.H & Ahamd, G.E & El Shenawy, E.T, 2004. "Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks," Renewable Energy, Elsevier, vol. 29(3), pages 443-457.
    2. Mellit, A. & Benghanem, M. & Arab, A. Hadj & Guessoum, A., 2005. "An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria," Renewable Energy, Elsevier, vol. 30(10), pages 1501-1524.
    3. de Blas, M.A & Torres, J.L & Prieto, E & Garcı́a, A, 2002. "Selecting a suitable model for characterizing photovoltaic devices," Renewable Energy, Elsevier, vol. 25(3), pages 371-380.
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